Energy burden is the percentage of household income spent on electricity, natural gas, and other fuels. This visualization explores the average energy burden of the low- to moderate-income (LMI) households in a county.
When comparing among disparate data sets, it is important to choose a common unit of spatial analysis. Here, we chose the county level because this was the highest resolution available for some of the included data. While some data, such as Heating Degree Days, were available at the census tract level, we aggregated to county level to avoid confusion in analysis and spurious variability.
A pairwise correlation analysis was performed to understand the relative correlations of the factors in our dataset that contribute to energy burden.
National Renewable Energy Laboratory (NREL)
Data were obtained from the National Renewable Energy Laboratory (NREL) Solar for All project. The NREL Solar for All project provided monthly electricity expenditures for all households and LMI households (NREL n.d.). This electricity expenditure data was originally collected via the American Community Survey (ACS). It was then spatialized via the Low-income Energy Affordability Dataset (LEAD) project and provided at the level of census tracts (DOE EERE 2017). Sigrin and Mooney (2018) aggregated the data to county-level to remove spurious variability. Additionally, the NREL Solar for All project provided the type and cost of the most common fuels used in home heating (NREL n.d.). These most common home heating fuel type data were derived from the ACS 5-yr estimate 2009-2013; whereas home heating fuel costs data were derived from the 2017 EIA Residential Energy Consumption Survey (RECS), reflecting prices in the Midwest during the 2016-2017 winter season. To provide more up-to-date and detailed data on home heating fuel types and costs, beyond the most common type in each county, we also utilized ACS, 5-year estimates, 2013-2017 and the Energy Information Administration (EIA) Table WF01 (EIA 2019). Finally, the NREL REPLICA study provided the Heating Degree Day (HDD) data, which was derived from National Solar Resource Database (NSRDB) 1998-2015 solar irradiance data (Mooney and Sigrin 2018).
Precursor data sets
Since energy burden is a complex metric, this visualization also includes several precursor data sets, which were sourced from multiple online databases. These data sets include median household incomes, income limits that define LMI households, the numbers of all households and LMI households, and the monthly electricity expenses for all households and LMI households. Estimates for median household income, homes built before 1979, and fraction of attached homes were obtained from the ACS, 5-year estimates. Following Sigrin and Mooney (2018), the income thresholds used for defining LMI households were downloaded from the Department of Housing and Urban Development (HUD) Community Development Block Grant (CDBG) program (HUD 2017). CDBG income limits are calculated annually to determine eligibility for housing assistance programs in the U.S. Here, we examined the 2017 income limits for a 4-person household. The number and percentage of households qualifying as LMI were downloaded from the NREL Solar for All project (NREL n.d.).
Energy Information Administration (EIA) (2019) Table WF01: Average Consumer Prices and Expenditures for Heating Fuels During the Winter [Online]. Available at: https://www.eia.gov/outlooks/steo/tables/pdf/wf-table.pdf (Accessed August 8, 2019).
Mooney, M. and Sigrin, B. (2018) Rooftop Energy Potential of Low Income Communities in America REPLICA. National Renewable Energy Laboratory. https://dx.doi.org/10.7799/1432837Available at: https://data.nrel.gov/submissions/81 (Accessed June 21, 2019).
National Renewable Energy Laboratory (NREL) (n.d.) Solar For All [Online]. Available at: https://maps.nrel.gov/solar-for-all/
Sigrin, B.O. and Mooney, M.E. 2018 Rooftop Solar Technical Potential for Low-to-Moderate Income Households in the United States (No. NREL/TP-6A20-70901). National Renewable Energy Lab (NREL), Golden, CO (United States).
U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (DOE EERE) (2017) Low-Income Energy Affordability Data (LEAD) Tool [Online]. Available at: https://catalog.data.gov/dataset/clean-energy-for-low-income-communities-accelerator-energy-data-profiles-2fffb
U.S. Department of Housing and Urban Development (HUD) (2017) Income Limits [Online]. Available at: https://www.huduser.gov/portal/datasets/il.html#2017 (Accessed June 14, 2019).
- The EPA provides this guide on improving energy efficiency of older and historic homes.
- DSIRE, the Database of State Incentives for Renewables and Efficiency, catalogs the many programs that can assist homeowners in making energy-efficiency improvements. Find programs available to you.
Heating Fuel Type:
- There are numerous considerations, economically and environmentally, with any heating fuel choice. For any community, a careful and well-informed analysis could indicate surprising choices for an ideal mix of heating fuels. For example, a study by the Rocky Mountain Institute found that for Chicago, IL, programs are needed now to switch to electricity for space heating, even when that electricity is generated from natural gas at the power plant (Billimoria, et al. 2018).
- When considering the costs of home heating fuels, it is important to note that home heating fuel prices vary based upon seasonal demand, crude oil costs, local market competition, and regional differences in operation and transportation costs (EIA 2018). Home heating fuel prices are likely to have changed since the data in this visualization were collected and will continue to do so. Check out the EIA for short-term fuel price outlooks.